Transgenerational plasticity affects fitness and mediates local adaptation

Transgenerational plasticity affects fitness and mediates local adaptation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Transgenerational plasticity affects fitness and mediates local adaptation View ORCID Profile Haley A. Branch , View ORCID Profile Dylan R. Moxley , View ORCID Profile Amy L. Angert doi: https://doi.org/10.1101/2025.11.23.690023 Haley A. Branch 1 Ecology and Evolutionary Biology, Yale University , New Haven, Connecticut, 06511, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Haley A. Branch For correspondence: haley.branch{at}yale.edu Dylan R. Moxley 2 Biodiversity Research Centre and Department of Botany, University of British Columbia , Vancouver, British Columbia, V6T 1Z4, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dylan R. Moxley Amy L. Angert 2 Biodiversity Research Centre and Department of Botany, University of British Columbia , Vancouver, British Columbia, V6T 1Z4, Canada 3 Department of Zoology, University of British Columbia , Vancouver, British Columbia V6T 1Z4, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amy L. Angert Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Transgenerational plasticity (TGP) could be as decisive of a factor in phenotypic outcomes as allelic variation and within-generation plasticity. While TGP is often associated with priming offspring to stress in stable environments (where offspring and grandparents are likely to experience similar stresses), our results suggest that TGP is locally adaptive for offspring when grandparents are exposed to environments that match historical conditions, regardless of the offspring’s environment. Specifically, we find that Mimulus cardinalis populations from historically wet environments exhibit adaptive TGP in grand-offspring, via increased male and female fitness, only when grandparents experience wet treatments, while TGP is maladaptive when these grandparents experience dry treatments. In contrast, populations from historically dry environments show the opposite. Furthermore, we find that TGP can have a greater effect on both male and female fitness than allelic differences and within-generation plasticity. Our results indicate an additional way that phenotypes arise through local adaption and sheds light on the potential for how rapid evolution might occur. Introduction The age-old debate of nature versus nurture has a long history in evolutionary biology, which has sought to understand the relative roles and interactions of genetic variation and responses to environmental variation (plasticity) in creating the phenotypic variation on which selection acts. We now appreciate that phenotypic differences caused by environmental variation can be inherited, not genetically, but through a process called transgenerational plasticity (TGP; defined here as an environmentally induced epigenetic phenotype that extends across three or more generations; 1-3). These inherited phenotypic differences are particularly significant when they alter responses to stress. Transgenerational plasticity could be a critical factor in eliciting stress responses and rapid adaptation ( 3 ) because evolution may act on allelic variation that causes TGP instead of allelic variation in the loci underlying a trait ( 2 ). Furthermore, epigenetic modifications have been shown to increase gene mutation rate and therefore have the capacity to increase the rate of evolution ( 2 ). Transgenerational plasticity has been shown to be advantageous when climates are predictable across years, in contrast to within-generation plasticity that evolves in variable climates ( 4 - 7 ). Therefore, it is likely that certain populations are more predisposed to evolve TGP in the first place. Studies suggest that TGP could help buffer populations from climate change, giving grand-offspring an advantage when experiencing the same stress as their grandparents ( 8 ). Alternatively, TGP could be maladaptive with rapid change, if phenotypes are primed to respond to past climates rather than present ones ( 6 ; 9 ), or if stress accumulation occurs ( 10 , 11 ). However, the role that non-genetic inheritance plays in rapid evolutionary responses is not well understood, particularly in wild populations of plants, yet it will likely be increasingly more important in the future as climate change intensifies. Most studies on non-genetic inheritance have focused on maternal effects across two generations (parent and offspring phenotypes). This type of non-genetic inheritance has largely been attributed to provisioning during embryo development or its proxy, zygote size ( 12 , 13 ) and those effects on offspring. Indeed, a recent review found only 11% of epigenetics studies (across plants and animals) examined more than two generations ( 14 ). Furthermore, studies that go beyond two generations have focused primarily at the molecular level, inbred lines, or clonal organisms ( 7 , 15 ). Therefore, little is known about the relative importance of TGP for determining phenotypic diversity compared to within-generation plasticity and allelic variation ( 7 ). It is generally assumed that genetics and within-generation plasticity play a much greater role in determining phenotypes than epigenetics, because epigenetic marks are shed quickly, particularly in the absence of continued stress ( 16 ). However, we are beginning to uncover that this is not always the case, particularly with plants ( 7 ). Thus, empirical evidence of how TGP (three or more generations) is expressed and evolves in wild populations is limited. Here we use Mimulus (Erythranthe) cardinalis (scarlet monkeyflower) to investigate population-level differences in the lasting phenotypic consequences of environmental stress across three generations. Using the resurrection approach where seed populations from ancestors and descendents are refreshed and compared in a common garden ( 17 ), we also assess whether TGP affects rapid evolution during a severe drought across the species’ range. We found previously that populations from climatically distinct regions evolved rapidly in response to this climatic event, largely towards dehydration avoidance ( 18 ; 19 ). However, we also observed lagging phenotypic effects from climates from two-years prior ( 9 ) and differences among regions in the extent and direction of trait evolution. Because northern populations reside in historically less variable climates than southern populations, we hypothesized they would be more likely to exhibit TGP. Following a refresher generation in benign conditions, we created a multi-generational (three selfed generations) and multi-factorial stress (wet and dry) design using both pre-drought (wild ancestors) and post-drought (wild descendants) plants ( Fig 1 ). We measured traits related to phenology, biomass allocation, and components of reproduction in the final generation to ask how exposure to water stress in grandparents affects trait expression and fitness in grand-offspring. We ask: 1) Does TGP exist?; 2) Does TGP differ across regions?; 3) Is TGP adaptive?; 4) Does TGP evolve rapidly during severe drought?; and 5) What is the relative contribution of TGP to phenotypic variation compared to rapidly evolved genetic differences and within-generation plasticity? Download figure Open in new tab Figure 1. Schematic showing experimental design. Upper left shows the drought severity map for Oregon and California relative to four focal populations (two northern–blue and two southern– pink) prior to drought (July 13, 2010) and at the peak of the drought (July 15, 2014). Drought levels are not dry (none), abnormally dry (0), moderate drought ( 1 ), severe drought ( 2 ), extreme drought ( 3 ), and exceptional drought ( 4 ) and were obtained from the National Drought Mitigation Center, USDA & NOAA ( 50 ). Upper right panel shows the historical averages (30-years prior) for annual precipitation (bio12) and interannual variability in precipitation seasonality (coefficient of variation of bio15) for northern and southern populations. In the bottom panel, ten individuals each from northern and southern populations for both pre-drought and peak-drought were refreshed in a greenhouse under neutral, wet conditions. These plants were then grown for three generations (G1, G2, and G3) in a multifactorial design of wet (blue) or dry (orange) treatments. The end result is eight distinct environmental treatment trajectories (n=1920). Results Prior to addressing our questions, we first conducted a principal components analysis of the six traits of interest: flowering date, flower number, pollen viability, seed set, specific leaf area (SLA), and germination time to collapse multivariate trait data into major orthogonal axes of variation ( Fig. 2A ). PC1 (24%) was associated with reproduction and its components, while PC2 (21%) was associated with growth traits. Download figure Open in new tab Figure 2. Principal components analysis of traits and their differences in transgenerational plasticity across regions. A) PCA of six traits: germination date, flowering date (FD), flower number, pollen viability, seed set, and specific leaf area (SLA). B) Reproductive consequences and advantages (PC1) of grandparental treatment across northern (navy solid) and southern (pink dashed) populations exposed to wet or dry treatments. C) Pollen viability is differentially affected by grandparental exposure to wet or dry treatments between northern and southern populations. D) Seed set per flower changes due to grandparental exposure to wet or dry treatments and this differs between northern and southern populations. E) Evolutionary differences (Pre- and Peak-drought) between northern and southern regions and grandparental wet (blue dashed) or dry (orange solid) treatment exposure. We used the PC1 and PC2 axes to address questions 1) does TGP exist and 2) are there differences in TGP across regions. A significant effect of the grandparent environment on offspring phenotype PC score supports the existence of TGP. We find that grandparent environment significantly affected grand-offspring reproduction components (PC1), and that its effect differed between southern and northern populations (region * grandparent treatment p < 0.001; Fig. 2B , Table S1). This resulted in offspring from northern populations (where historical environments are wet; Fig. 1 ) having greater reproduction when their grandparents experienced wet environments compared to dry environments. In contrast, offspring from southern populations (where historical environments are dry; Fig. 1 ) showed the opposite advantage, having greater reproduction when grandparents were in dry environments compared to wet environments. We also find an effect of treatment (p = 0.002), resulting in greater reproduction of grand-offspring in the dry compared to the wet treatment, which is likely driven by delayed phenology of plants in the wet treatment. However, there is no interaction with treatment and grandparental environment (p = 0.80). For PC2, we found that only the main effects of region (p < 0.001) and treatment (p = 0.03) affected growth traits (Fig S1A, Table S2). There are two ways in which TGP could be considered adaptive (question 3). The first is if higher fitness occurs in offspring that experience the same environment as their grandparents (i.e., when water treatments match between generations) compared to when environments are mismatched between generations, resulting in an interaction between current treatment and grandparent treatment. The second is if fitness is higher when treatments resemble the historical selective environment (i.e., when grandparent treatment matches climatic norms). When we examined pollen viability (male fitness) and maximum seed set (female fitness) specifically, we found identical results to PC1 ( Fig 2C-D and Table S3-4): offspring have higher male and female fitness when their grandparents grew in environments that resembled their home climates. Given the regional differences in TGP for PC1 (above), pollen viability, and seed set, regardless of current treatment, this suggests the latter form of local adaptation. Analysis of all individual traits can be found in supplemental materials (Fig S1B-E, Table S5-8). To examine whether TGP evolved rapidly during drought (question 4), we examined whether TGP changed from ancestor and descendant populations by testing for significant interactions with Year. We found a three-way interaction that suggested evolution of TGP differed between regions (three-way interaction p=0.03; Table S9). Prior to drought, grandparent treatment significantly altered reproduction in both regions in the locally adaptive pattern described above, but there is no clear effect of grandparental treatment pattern on offspring PC1 scores observed following drought in either region ( Fig 2E ; Table S9). However, contrasts of pre-drought and peak-drought trait expression were not significantly different within regions and treatment, leaving evidence for the evolution of TGP mixed. There was no relationship between grandparent treatment and year for PC2 (Fig S2, Table S10). Lastly, to determine the strength of the effect of TGP, genetic change, and within-generation plasticity on fitness (question 5), we examined the effect sizes of grandparent treatment (representing TGP), year (representing allelic change over time between pre-drought ancestors and post-drought descendent populations), and current treatment (representing within-generation plasticity), across all traits for each region separately (Table S11). For southern populations, pollen viability and seed set were most strongly affected by grandparental treatment (es = 0.21, 0.15 respectively; Fig. 3 ) compared to either current treatment (es = 0.09, 0.09) or year (es = 0.04, 0.08). In northern populations, grandparental treatment had the strongest effect for germination timing (es = 0.06; Fig. 3 ), but current treatment had a greater effect in most other traits. Importantly, grandparental treatment (es = 0.07) had almost the same effect on pollen viability as year (es = 0.08) in the north. Download figure Open in new tab Figure 3. Relative effect of allelic differences (Year), transgenerational plasticity (Grandparent treatment), and within-generational plasticity (Current treatment) on plant traits for northern (A) and southern (B) populations. Standardized effect sizes for germination date (Germ), specific leaf area (SLA), flowering date (FD), flower number (Flr Num), pollen viability (Pollen), and maximum seed set (Seed set) as response variables where derived from linear mixed models that consisted of year, grandparental treatment, and current treatment and their interactions as fixed effects. Germination date does not include within-generation plasticity because offspring had not undergone a treatment yet, therefore there are no interactions that include this variable either. Red bars involve transgenerational plasticity as the main effect (solid red) or in an interaction term (red background with interacting factor depicted by color of hashed lines and outline). Yellow bars indicate the main effect of year or in the interaction with within-generation plasticity as a blue hashed line. Blue bars indicate the main effect of within-generation plasticity. Discussion We found that transgenerational plasticity, attributable to epigenetic modifications inherited from grandparents, significantly affects both male and female fitness and can have greater fitness consequences than allelic or plastic sources of variation. These results are the first that we know of to empirically examine TGP in wild plants and how it contributes to phenotypic change during rapid evolution. We first addressed the existence of TGP and whether its expression evolved differently between regions with different historical precipitation regimes. Theory and empirical studies show that TGP evolves and is adaptive in predictable environments ( 4 , 5 , 20 ), where predictability is inferred by temporal autocorrelation. For example, populations of Drosophila mojavensis that occur in predictably variable climates showed greater evolution of TGP than populations with less autocorrelation ( 21 ). Other studies suggest that TGP evolves where it can prime offspring to perform better in the same stress ( 22 ). For example, droughted offspring of droughted grandparents or parents had greater growth rates than droughted offspring with no generational history of drought ( 23 ). In contrast to our prediction that northern populations would exhibit TGP while southern populations would not (based on climatic variability; Fig. 1 ), we found TGP in both regions and stronger TGP in the south. It is possible that interannual variability in precipitation seasonality is an inadequate metric of climatic predictability, and perhaps the southern region should be considered more stable. Southern populations have higher rates of within-generation plasticity in phenotypic traits ( Fig. 3 ; 9), which is generally attributed to heterogeneous environments (i.e., high rates of variability). However, Diaz et al. (2021) found that the Drosophila mojavensis climate that was more variable was also more predictable and this was associated not only with TGP evolution but also with within-generation plasticity ( 21 ). While we did not find differences in TGP that matched predictions based on precipitation variability, we did find that TGP can be adaptive (question 3). We find that TGP is advantageous when the environment of the grandparent matches historical climate, regardless of the environment of the offspring ( Fig 2B-D ). This is contrary to how TGP is generally expected to work (above). In our experiment, grandparents did not prime offspring to perform better in the same environment, but rather, grandparents increased fitness of their grand-offspring when the grandparent treatment simulated their population’s historical environment. This result is similar to local adaptation with fitness trade-offs: TGP is only adaptive when the grandparent treatments match native environments, while maladaptive in foreign ones. A similar result was found in Lupinus angustifolius across two generations, where populations from wet climates exhibited greater seed mass when parents were exposed to wet treatments compared to dry treatments, though no such relationship was found for populations from semiarid climates ( 24 ). Our results are the first that we know of that describe grandparentally derived plasticity responding in this manner. Transgenerational plasticity can also become maladaptive due to strong cumulative effects. Three generations of harsh temperatures resulted in significantly lower fitness in Arabidopsis ( 6 ) and different phenotypic outcomes occurred depending on the number of generations exposed ( 25 ). Similar to mutation accumulation, recurrent stress across generations can seemingly result in an accumulation of maladapted epialleles. This is not always the case, as sometimes cumulative effects can result in greater priming, such as when three generations of drought resulted in greatest seedling development in the herb Polygonum persicaria ( 23 ). Groot et al . (2016) conducted a study on salt stress across three generations in an inbred line of Arabidopsis and found that when only the parental generation was exposed to stress the progeny had small rosettes and flowered later, characterized as maladaptive, but this was alleviated when both grandparents and parents experienced salt stress ( 26 ). Our results do not suggest cumulative effects of TGP as is evident by the lack of an interaction between current treatment and grandparent treatment ( Fig. 2 ). Indeed, here we find that the fitness direction was determined by the grandparent treatment regardless of the current treatment entirely. We have not included parental treatment as an interacting factor in this analysis, which would be an appropriate next step for future research. Teasing apart the main source of phenotypic variation (i.e., G x E TGP x E Within-generation plasticity ) has been of great debate since the recognition of TGP ( 7 , 8 ). The significant effect that TGP has on pollen viability and seed set in our study, particularly for southern populations, has the potential for great evolutionary consequences. Highly conserved epigenomic modifications have been associated with pollen development and seed production in Arabidopsis ( 27 , 28 ). Furthermore, epiallelic variation diversifies at greater rates than genetic mutation ( 2 ), and therefore contributes a significant role for phenotypic differences and potentially allows for faster evolution ( 3 , 29 - 32 ). Our results show that TGP had a greater effect on fitness components than recently evolved allelic differences in southern populations ( Fig. 3 ) and a similar effect for pollen viability in northern populations. These results could lead to differences in the rate of rapid evolution (faster in the south and slower in the north) that we found previously ( 9 , 18 , 19 ). Lastly, we did not find clear evidence that TGP is rapidly labile to stress perturbation. Although we do not observe the same magnitude of differences in wet and dry grandparental treatments in the peak-drought descendent population compared to the pre-drought ancestral population, there is no strong evidence that this represents a loss of TGP because pre-vs. peak-drought differences are not statistically supported in either region or treatment ( Fig 2E ; Table S9). Clearly TGP evolves over some timescales because of the observed regional differences in its expression, but in this study TGP seems to be more persistent or robust over the short term in comparison to the allelic turnover that contributes to evolution in drought tolerance traits that we observed in prior studies ( 18 , 19 ). We are not aware of any other studies that have examined whether TGP has the capacity to rapidly evolve and therefore this represents an area of opportunity for further investigation. Our results provide new insight on how phenotypic variation arises through transgenerational plasticity. Grandparental environments can have significant consequences for both male and female fitness, which we would not have known had we exclusively examined phenological, vegetative, and floral traits. This work emphasizes the need to incorporate fitness effects into studies on transgenerational plasticity and epigenetics. We find that the capacity to express transgenerational plasticity and the significance of that expression is genetically determined (regional differences) and that it is only adaptive when conditions reflect historical climate patterns. We show clear evidence that future studies on transgenerational plasticity should examine the phenotypic consequences of environments from generations beyond the parental environment. Materials and Methods Study system Mimulus cardinalis Spach (Erythranthe cardinalis Doughl. ex Benth; 33-36) is an herbaceous, hummingbird-pollinated perennial plant that occurs within riparian areas across southwestern North America from central Oregon to northern Baja California ( 37 ). Populations (Table S12) in the northern portion of the range experience higher mean annual precipitation and lower precipitation variability across years compared to populations in the south, which experience lower mean annual precipitation and more variable precipitation patterns across years ( Fig. 1 ; Calscape, accessed 2019). From 2012-2015 a record-breaking 4-year drought impacted the entire range ( 38 ). Seeds of M. cardinalis were collected ( 18 ) prior to the drought onset in 2010 (ancestral genotypes) and collected at the peak of the drought (2014 or 2015, depending on site), representing post-drought descendant genotypes. Growth Conditions Wild seeds were refreshed in a common greenhouse with 27/15°C day/night temperatures, 12-hour days, and saturating water. Germination of these seeds was not affected by the invisible fraction problem ( 17 , 39 ). We used the resurrection approach to compare ancestor and descendant progeny. Refreshed seeds were sown in 233ml square pots filled with a 2:1 mix of fine-grade sand (QUIKRETE® Premium Play Sand® No. 1113) and Sunshine Mix #4 without arbuscular mycorrhizal fungi (AMF; Sun Gro Horticulture, Agawam, MA), with 0.52g per pot of evenly distributed Osmocote Plus slow-release fertilizer (Scotts Co LLC, Marysville, Ohio). Seeds were misted for two minutes, four times a day until germination. Afterward plants were bottom watered to field capacity until established, characterized as the development of three sets of leaves. Plants were then placed in a wet (maintained at field capacity for the experiment duration) or a dry treatment (held just above wilting point). To ensure all plants experienced the same level of abiotic stress, the dry treatment was tailored to each plant’s wilting point, which was visually assessed multiple times per day; water was supplemented when wilt occurred. Greenhouse conditions were set to 14-hour day, 28/18°C day/night temperatures, with natural light being supplemented by LED lights as needed. Plants were grown in their respective treatments through fruit maturation; thus, the duration of the experiment differed depending on individual plant phenology. This ensured that full seed development occurred within the treatment conditions. The same growth conditions and treatment categories were used across all three generations. Establishment of generations Five families (seeds derived from a single fruit) from each population and year were selected randomly and then introduced to the two treatments (grandparental; n = 80; Fig.1 ). Flowers were self-pollinated during their treatment and fruits matured within the treatment. To replicate full-siblings, six seeds from each grandparental generation plant were sown (three in wet and three in dry) to establish the second generation (parental; n = 480; Fig.1 ). Flowers were again self-pollinated and fruits matured under treatment. For the final generation, four seeds from each parent plant were sown and then two each were grown in wet or dry treatment (offspring; n = 1920; Fig. 1 ). Measurements In each generation, pots were checked daily and the date of germination/emergence was recorded. At the time of first flower, the most recently fully developed leaf not subtending the flower was removed and measured for specific leaf area (cm 2 /g; SLA). SLA was calculated as the surface area of one side of the leaf divided by the dry mass of the leaf. Freshly harvested leaves had projected area measured with a LI-300C (LiCor Inc., Lincoln, NB) and, following drying in a 50°C oven for two days, were weighed. Date of first flower opening was recorded and after fruit ripening, plant height, stem diameter at base, number of branches, and number of leaves were recorded. Plants that had not flowered after 120 days (∼4 months) were not included in flowering date. This cut off was determined based on typical growing season length. Pollen was collected in half the third generation from one undehisced mature anther from the first flower and placed in Alexander’s Triple Stain ( 40 ). An average of 300 pollen grains were counted per individual and the percent aborted versus mature were documented (later converted to the amount viable). The first flower was saturated with pollen by placing a dehisced anther fully on the stigma and pressing the stigma together to dislodge pollen. Stigmas were monitored the following day to ensure fertilization had been complete. At maturation, fruits were collected and the total seeds were weighed. Average seed weight (0.0232738mg) was determined by counting and weighing the total seeds produced in 444 fruits for individuals across all three generations. Seed totals were estimated based on their total weight relative to the average seed weight. Statistical Analysis We first performed a correlation assessment of all traits using the corr function in R software (version 4.0.4; 41). By doing this we reduced the number of traits to six focal ones: germination date, flowering date, flower number, pollen viability, seed set, and specific leaf area (SLA). We then conducted a principal components analysis using prcomp on correlation matrix ( 41 ) of the six traits. We used PC1 and PC2 scores to address whether TGP exists (question 1), if there are differences in TGP between regions (question 2), and whether it is adaptive (question 3). We were interested in whether TGP existed pre-drought, thus we used data from ancestral plants (594 observations) to conduct 3-way linear mixed effects models using the lmer function from ‘lme4’ package ( 42 ). We included region, grandparental treatment, and current treatment as fixed effects. Parental treatment and replicate (only two complete replicates) were non-interacting fixed effects, while grandparental ID was a random effect. Prior to building each model, orthogonal contrasts for each predictor were ensured by setting the contrasts to ‘ contr . sum’ . We examined each model for homoscedasticity and normality using qqplot (generated from qqmath function in the ‘lattice’ package; 43) and histograms of the residuals ( hist function in base R). PC2 was left-skewed, so the data were transformed using cubic root. To test model significance we used type-III Anova (‘car’ package; 44) and conducted planned pairwise comparisons using emmeans and contrast ( 45 ), removing contrasts we were not interested in. Figures were generated using ‘visreg’ residuals and ‘ggplot2’ with means and standard errors ( 46 ). To further address whether TGP is adaptive (question 3) we performed the same analyses as above, but specifically examined pollen viability (male fitness; 331 observations) and seed set (female fitness; 820 observations). Pollen viability was zero-inflated with differing total pollen grains per sample, thus glmmTMB ( 47 , 48 ) with a beta binomial was used. The same tests for significance were used as for PC1 and PC2 models. To address whether TGP evolves (question 4) we included data derived from peak-drought descendants (total 1209 observations) in models examining differences in PC1 and PC2. Here the fixed effects were region, year, grandparent treatment and their interactions, as well as the main effects of current treatment, parental treatment, and replicate. The same random effect as the previous model was used (grandparental ID). PC2 was left-skewed and again cubic root transformed. The same methods for model significance and comparisons were used as the previous models. To assess the relative strength of TGP, allelic differences, and within-generation plasticity (question 5), we examined the effect sizes of grandparent treatment, current treatment, and year from mixed models on all six traits. Regions were separated (north = 761 observations; south = 807 observations) and models consisted of the three factors and their interactions, and parent treatment and replicate as main effects. Grandparental ID was a random effect. We used standardize_parameters function from the ‘effectsize’ package ( 49 ). Several traits had to be transformed; all transformations and distributional families for each response variable are given in Table S13. Acknowledgments We thank Dr. Katie Marshall for feedback on the initial experimental design. Many individuals assisted in data collection: Simi Badyal, Cara-Lynn Branch, Sam Dang, Lydia Fong, Julien Grebert, Carly Hilbert, Amy Liu, Hayat Mahdjoub, Annie McLeod, Stephanie McPhail, Bryn Murphy, Burak Ozkan, Gundeep Pannu, David Qi, Maya U Schueller Elmers, Mia Waters, Maggie Yang, and Noah Zovickian. Funding for this research was provided by a National Science Foundation (NSF) PRFB 2305993, Natural Sciences and Engineering Research Council (NSERC) of Canada PDF 578168, NSERC PGS-D, and Rick Hansen Man in Motion Fellowship from the University of British Columbia to H.B., and NSERC Discovery grant AWD-010335 to A.A. Footnotes Competing Interest Statement: No competing interests to declare. References 1. ↵ L. R. Baugh , T. 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